A Human Monoclonal IgG That Binds Aβ Assemblies and Diverse Amyloids Exhibits Anti-Amyloid Activities<i>In Vitro</i>and<i>In Vivo</i>
Bibliographic record
Abstract
Alzheimer's disease (AD) and familial Danish dementia (FDD) are degenerative neurological diseases characterized by amyloid pathology. Normal human sera contain IgG antibodies that specifically bind diverse preamyloid and amyloid proteins and have shown therapeutic potential in vitro and in vivo. We cloned one of these antibodies, 3H3, from memory B cells of a healthy individual using a hybridoma method. 3H3 is an affinity-matured IgG that binds a pan-amyloid epitope, recognizing both Aβ and λ Ig light chain (LC) amyloids, which are associated with AD and primary amyloidosis, respectively. The pan-amyloid-binding properties of 3H3 were demonstrated using ELISA, immunohistochemical studies, and competition binding assays. Functional studies showed that 3H3 inhibits both Aβ and LC amyloid formation in vitro and abrogates disruption of hippocampal synaptic plasticity by AD-patient-derived soluble Aβ in vivo. A 3H3 single-chain variable fragment (scFv) retained the binding specificity of the 3H3 IgG and, when expressed in the brains of transgenic mice using an adeno-associated virus (AAV) vector, decreased parenchymal Aβ amyloid deposition in TgCRND8 mice and ADan (Danish Amyloid) cerebral amyloid angiopathy in the mouse model of FDD. These data indicate that naturally occurring human IgGs can recognize a conformational, amyloid-specific epitope and have potent anti-amyloid activities, providing a rationale to test their potential as antibody therapeutics for diverse neurological and other amyloid diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".